Clinical Decision Support Algorithm to Predict Diabetic Retinopathy
Validating a Clinical Decision Support Algorithm Developed With Demographic, Co-morbidity, and Lab Data to Diagnose, Stage, Prevent, and Monitor a Patient's Diabetic Retinopathy
1 other identifier
observational
500
0 countries
N/A
Brief Summary
Diabetic retinopathy (DR), a complication of diabetes, is a leading cause of blindness among working-aged adults globally. In its early stages, DR is symptomless, and can only be detected by an annual eye exam. Once the disease has progressed to the point where vision loss has occurred, the damage is irreversible. Consequently, early detection is quintessential in treating DR. Two barriers to early detection are poor patient compliance with the annual exam and lack of access to specialists in rural areas. This research is focused on developing and validating new, cost-effective predictive technologies that can improve early screening of DR. Our overall objective is to develop and implement an entire suite of tools to detect diabetes complications in order to augment care for underserved rural populations in the US and internationally.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Feb 2022
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
December 6, 2018
CompletedFirst Posted
Study publicly available on registry
December 10, 2018
CompletedStudy Start
First participant enrolled
February 1, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
August 1, 2022
CompletedStudy Completion
Last participant's last visit for all outcomes
July 1, 2023
CompletedSeptember 18, 2020
September 1, 2020
6 months
December 6, 2018
September 17, 2020
Conditions
Outcome Measures
Primary Outcomes (1)
Diabetic retinopathy indicator (yes/no)
Diabetic patients with 362.0x ICD-9 codes are classified as DR patient
March, 2019
Interventions
Demographic variables: gender, race, marital status, urban rural status. Co-morbidity variable: neuropathy, nephropathy, peripheral circulatory, ketoacidosis, hyperosmolarity Lab tests variables: alanine aminotransferase (ALT), albumin serum, anion gap, aspartate aminotransferase, blood urea nitrogen, calcium serum, chloride serum, creatinine serum, glucose serum plasma, hematocrit, hemoglobin, mean corpuscular hemoglobin concentration (MCHC), mean platelet volume (MPV), potassium serum, protein total serum, red blood cell (RBC) count, sodium serum, white blood cell (WBC) count
Eligibility Criteria
The cohort will be selected from diabetic patients that have Electronic Medical Records in Harold Hamm Diabetes Center.
You may qualify if:
- For diabetic patients with DR:
- With 250.xx diabetes and 362.0x DR ICD-9 codes
- All variables are complete within the observation window
- For diabetic patients without DR:
- With 250.xx diabetes ICD-9 codes
- Without 362.0x DR ICD-9 codes
- All variables are complete within the observation window
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Oklahoma State University Center for Health Scienceslead
- Oklahoma State Universitycollaborator
- University of Oklahomacollaborator
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Executive Director
Study Record Dates
First Submitted
December 6, 2018
First Posted
December 10, 2018
Study Start
February 1, 2022
Primary Completion
August 1, 2022
Study Completion
July 1, 2023
Last Updated
September 18, 2020
Record last verified: 2020-09